The present invention generally relates to the imaging field of object detection and recognition. More specifically, the present invention relates to a system for the recognition of objects of interest among other objects in a captured image.
The detection and recognition of objects of interest among other objects within a captured image is a field that has been the subject of intense research since the emergence of digital imaging technology. A variety of systems has been produced which attempt to accurately and precisely detect objects of interest among other objects. In particular, one application of such systems is in the detection and recognition of road signs.
Initially, the detection and recognition of road signs appears to be a well-bounded problem and the solution would be quite simple. That is, road signs tend to occur in standardized positions in traffic scenes. Further, their shape, color and pictograms are known because of national, regional, and local transportation standards. However, the solution to such a problem with an object detection and recognition system is not as simple as it may initially seem.
Indeed, a variety of difficulties are faced by such an object detection and recognition system. An image captured for the purpose of road sign recognition will be acquired from a moving car and the sign size and orientation will change with distance as it may be skewed and rotated. Additionally, many objects will be present in highly complex captured images such as pedestrians, other vehicles, bushes, buildings, and other signs. Some of these other objects may be very similar in shape, pattern, and color to a road sign. To further complicate matters, in many areas road signs exist in many variants that often vary significantly from the legally established standard for that nation, region, or locale. Lastly, as with all applied computer vision systems, the implemented system must be suitable for real-time implementation requiring the processing of large amounts of information in a shortened time-frame.
One example of an object detection and recognition system utilizes a single camera. The images of a single camera are fed into a machine-learning algorithm such as a neural network. The machine-learning algorithm, which has previously been trained by a database of road images, attempts to classify the captured image and its contents thereby allowing a set of objects of interest to be detected as well as recognized. However, this statistical approach to machine learning requires the acquisition of large image databases that is both time-consuming and expensive. Further, this approach is not entirely effective at remedying the problems discussed above with respect to vibration, color variation, obscuring of images, complexity of captured images, and variation of road signs from established standards.
In addition to the use of machine learning algorithms in a statistical approach, another method for detecting objects of interest and subsequently recognizing them is the use of color to aid object segmentation from the background imagery. However, the fast and robust color segmentation of objects is difficult as the color wavelength arriving at the camera is not invariant to the intensity of the incident light and age of the sign as they fade with time.
As demonstrated above by the explanation of existing systems, many of the solutions to detecting and recognizing objects of interest in a captured image are not optimal. Therefore, a system for detecting and recognizing objects of interest in a captured image that is capable of operating in various environments is needed.